Dataset Information

Evaluation of 3D Chromatin Interactions Using Hi-C.

ABSTRACT: The invention of Hi-C has greatly facilitated 3D genome research through an unbiased probing of 3D chromatin interactions. It produces enormous amount of sequencing data that capture multiscale chromatin conformation structures. In the last decade, numerous computational methods have been developed to analyze Hi-C data and predict A/B compartments, topologically associating domains (TADs), and significant chromatin contacts. This chapter introduced the iHiC package that provides several utilities to facilitate Hi-C data analysis with public software and demonstrated its application to a Hi-C dataset generated for mouse embryonic stem (ES) cells.

SUBMITTER: Hu G

PROVIDER: S-EPMC7441592 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Evaluation of 3D Chromatin Interactions Using Hi-C.

Hu Gangqing G

Methods in molecular biology (Clifton, N.J.) 20200101

The invention of Hi-C has greatly facilitated 3D genome research through an unbiased probing of 3D chromatin interactions. It produces enormous amount of sequencing data that capture multiscale chromatin conformation structures. In the last decade, numerous computational methods have been developed to analyze Hi-C data and predict A/B compartments, topologically associating domains (TADs), and significant chromatin contacts. This chapter introduced the iHiC package that provides several utilitie ...[more]

PMID: 31960372

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Project description:BackgroundRegulatory elements such as promoters, enhancers, and insulators interact each other to mediate molecular processes. To capture chromatin interactions of regulatory elements, 3C-derived methods such as Hi-C and Micro-C are developed. Here, we generated and analyzed Hi-C, Micro-C, and promoter capture Micro-C datasets with different sequencing depths to study chromatin interactions of regulatory elements and nucleosome positions in human prostate cancer cells.ResultsCompared to Hi-C, Micro-C identifies more high-resolution loops, including ones around structural variants. By evaluating the effect of sequencing depth, we revealed that more than 2 billion reads of Micro-C are needed to detect chromatin interactions at 1 kb resolution. Moreover, we found that deep-sequencing identifies additional long-range loops that are longer than 1 Mb in distance. Furthermore, we found that more than 50% of the loops are involved in insulators while less than 10% of the loops are promoter-enhancer loops. To comprehensively capture chromatin interactions that promoters are involved in, we performed promoter capture Micro-C. Promoter capture Micro-C identifies loops near promoters with a lower amount of sequencing reads. Sequencing of 160 million reads of promoter capture Micro-C resulted in reaching a plateau of identifying loops. However, there was still a subset of promoters that are not involved in loops even after deep-sequencing. By integrating Micro-C with NOMe-seq and ChIP-seq, we found that active promoters involved in loops have a more accessible region with lower levels of DNA methylation and more highly phased nucleosomes, compared to active promoters that are not involved in loops.ConclusionWe determined the required sequencing depth for Micro-C and promoter capture Micro-C to generate high-resolution chromatin interaction maps and loops. We also investigated the effect of sequencing coverage of Hi-C, Micro-C, and promoter capture Micro-C on detecting chromatin loops. Our analyses suggest the presence of distinct regulatory element groups, which are differently involved in nucleosome positions and chromatin interactions. This study does not only provide valuable insights on understanding chromatin interactions of regulatory elements, but also present guidelines for designing research projects on chromatin interactions among regulatory elements.

Dataset Information

Evaluation of 3D Chromatin Interactions Using Hi-C.

Publications

Evaluation of 3D Chromatin Interactions Using Hi-C.

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